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Technical Prerequisites of Population-Based Imaging

  • Sergios Gatidis
  • Fabian Bamberg
Chapter
Part of the Medical Radiology book series (MEDRAD)

Abstract

The main goal of population-based imaging is to gain insight into physiological and pathophysiological processes of individuals by assessing corresponding morphological and functional changes in the general population using imaging techniques. This approach is fundamentally different from the usual clinical approach, where the individual examination is in the center of attention and usually not directly related or compared to population-based imaging data. Therefore, specific technical and organizational prerequisites have to be met in order to successfully conduct population-based imaging studies. In this chapter, these prerequisites will be discussed concerning the underlying imaging modalities as well as aspects of data storage and data processing.

Keywords

Intravenous Contrast Agent Quality Assurance Test Efficient Analysis Method Ideal Imaging Modality Actual Data Acquisition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Literature

  1. Bamberg F, Kauczor HU, Weckbach S, Schlett CL, Forsting M, Ladd SC et al (2015) Whole-body MR imaging in the German National Cohort: rationale, design, and technical background. German National Cohort MRI Study Investigators. Radiology. 2015;277(1):206–220Google Scholar
  2. Friedman L, Stern H, Brown GG, Mathalon DH, Turner J, Glover GH et al (2008) Test-retest and between-site reliability in a multicenter fMRI study. Hum Brain Mapp 29(8):958–972CrossRefPubMedPubMedCentralGoogle Scholar
  3. Gatidis S, Schlett CL, Notohamiprodjo M, Bamberg F (2016) Imaging-based characterization of cardiometabolic phenotypes focusing on whole-body MRI-an approach to disease prevention and personalized treatment. Br J Radiol 89(1059):20150829CrossRefPubMedPubMedCentralGoogle Scholar
  4. Hegenscheid K, Kuhn JP, Volzke H, Biffar R, Hosten N, Puls R (2009) Whole-body magnetic resonance imaging of healthy volunteers: pilot study results from the population-based SHIP study. Rofo 181(8):748–759CrossRefPubMedGoogle Scholar
  5. Medland SE, Jahanshad N, Neale BM, Thompson PM (2014) Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci 17(6):791–800CrossRefPubMedPubMedCentralGoogle Scholar
  6. Meyer J, Ostrzinski S, Fredrich D, Havemann C, Krafczyk J, Hoffmann W (2012) Efficient data management in a large-scale epidemiology research project. Comput Methods Programs Biomed 107(3):425–435CrossRefPubMedGoogle Scholar
  7. Petersen SE, Matthews PM, Bamberg F, Bluemke DA, Francis JM, Friedrich MG et al (2013) Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank – rationale, challenges and approaches. J Cardiovasc Magn Reson Off J Soc Cardiovasc Magn Reson 15:46Google Scholar
  8. Schlett CL, Hendel T, Hirsch J, Weckbach S, Caspers S, Schulz-Menger J et al (2016) Quantitative, organ-specific interscanner and intrascanner variability for 3 T whole-body magnetic resonance imaging in a multicenter, multivendor study. Invest Radiol 51(4):255–265CrossRefPubMedGoogle Scholar
  9. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME et al (2014) The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 8(2):153–182PubMedPubMedCentralGoogle Scholar
  10. Wurslin C, Machann J, Rempp H, Claussen C, Yang B, Schick F (2010) Topography mapping of whole body adipose tissue using a fully automated and standardized procedure. J Magn Reson Imaging : JMRI 31(2):430–439CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sergios Gatidis
    • 1
  • Fabian Bamberg
    • 1
  1. 1.Department of Diagnostic and Interventional RadiologyUniversity of TuebingenTübingenGermany

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